one noise variable, linear regression

## [1] "*************************************************************"
## [1] "one noise variable, linear regression"
## [1] "bSigmaBest 34"
## [1] "naive effects model"
## [1] "one noise variable, linear regression naive effects model fit model:"
## 
## Call:
## lm(formula = formulaL, data = trainData)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.2322 -0.6020  0.0120  0.5804  3.2574 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 0.001467   0.019623   0.075     0.94    
## n1          1.000321   0.038697  25.850   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8776 on 1998 degrees of freedom
## Multiple R-squared:  0.2506, Adjusted R-squared:  0.2503 
## F-statistic: 668.2 on 1 and 1998 DF,  p-value: < 2.2e-16
## 
## [1] " train rmse 0.87711349635425"
## [1] " application rmse 1.15239485807949"
## [1] "one noise variable, linear regression naive effects model train rmse 0.87711349635425"

## TableGrob (3 x 2) "arrange": 5 grobs
##   z     cells    name                grob
## 1 1 (2-2,1-1) arrange      gtable[layout]
## 2 2 (2-2,2-2) arrange      gtable[layout]
## 3 3 (3-3,1-1) arrange      gtable[layout]
## 4 4 (3-3,2-2) arrange      gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.140]
## [1] "one noise variable, linear regression naive effects model test rmse 1.15239485807949"

## TableGrob (3 x 2) "arrange": 5 grobs
##   z     cells    name                grob
## 1 1 (2-2,1-1) arrange      gtable[layout]
## 2 2 (2-2,2-2) arrange      gtable[layout]
## 3 3 (3-3,1-1) arrange      gtable[layout]
## 4 4 (3-3,2-2) arrange      gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.293]
## [1] "effects model, sigma= 34"
## [1] "one noise variable, linear regression effects model, sigma= 34 fit model:"
## 
## Call:
## lm(formula = formulaL, data = trainData)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.4236 -0.6781 -0.0004  0.6643  3.8923 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.465e-03  2.282e-02   0.064    0.949
## n1          5.098e-07  7.730e-04   0.001    0.999
## 
## Residual standard error: 1.014 on 1998 degrees of freedom
## Multiple R-squared:  2.177e-10,  Adjusted R-squared:  -0.0005005 
## F-statistic: 4.35e-07 on 1 and 1998 DF,  p-value: 0.9995
## 
## [1] " train rmse 1.01322809540058"
## [1] " application rmse 0.99564716364221"
## [1] "one noise variable, linear regression Laplace noised 34 train rmse 1.01322809540058"

## TableGrob (3 x 2) "arrange": 5 grobs
##   z     cells    name                grob
## 1 1 (2-2,1-1) arrange      gtable[layout]
## 2 2 (2-2,2-2) arrange      gtable[layout]
## 3 3 (3-3,1-1) arrange      gtable[layout]
## 4 4 (3-3,2-2) arrange      gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.446]
## [1] "one noise variable, linear regression Laplace noised 34 test rmse 0.99564716364221"

## TableGrob (3 x 2) "arrange": 5 grobs
##   z     cells    name                grob
## 1 1 (2-2,1-1) arrange      gtable[layout]
## 2 2 (2-2,2-2) arrange      gtable[layout]
## 3 3 (3-3,1-1) arrange      gtable[layout]
## 4 4 (3-3,2-2) arrange      gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.599]
## [1] "effects model, jacknifed"
## [1] "one noise variable, linear regression effects model, jackknifed fit model:"
## 
## Call:
## lm(formula = formulaL, data = trainData)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.4251 -0.6776 -0.0009  0.6645  3.8913 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.001465   0.022668   0.065    0.948
## n1          0.004279   0.038189   0.112    0.911
## 
## Residual standard error: 1.014 on 1998 degrees of freedom
## Multiple R-squared:  6.285e-06,  Adjusted R-squared:  -0.0004942 
## F-statistic: 0.01256 on 1 and 1998 DF,  p-value: 0.9108
## 
## [1] " train rmse 1.01322491166252"
## [1] " application rmse 0.99567998170435"
## [1] "one noise variable, linear regression jackknifed train rmse 1.01322491166252"

## TableGrob (3 x 2) "arrange": 5 grobs
##   z     cells    name                grob
## 1 1 (2-2,1-1) arrange      gtable[layout]
## 2 2 (2-2,2-2) arrange      gtable[layout]
## 3 3 (3-3,1-1) arrange      gtable[layout]
## 4 4 (3-3,2-2) arrange      gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.752]
## [1] "one noise variable, linear regression jackknifed test rmse 0.99567998170435"

## TableGrob (3 x 2) "arrange": 5 grobs
##   z     cells    name                grob
## 1 1 (2-2,1-1) arrange      gtable[layout]
## 2 2 (2-2,2-2) arrange      gtable[layout]
## 3 3 (3-3,1-1) arrange      gtable[layout]
## 4 4 (3-3,2-2) arrange      gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.905]

## [1] "********"
## [1] "one noise variable, linear regression JackknifeModel"
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.9791  0.9961  1.0000  1.0000  1.0050  1.0170 
## [1] 0.006594534
## [1] "********"
## [1] "********"
## [1] "one noise variable, linear regression NaiveModel"
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.111   1.140   1.148   1.149   1.159   1.186 
## [1] 0.01415858
## [1] "********"
## [1] "********"
## [1] "one noise variable, linear regression NoisedModel"
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.9797  0.9961  1.0000  1.0000  1.0050  1.0180 
## [1] 0.006668502
## [1] "********"

## [1] "*************************************************************"

one variable, linear regression

## [1] "*************************************************************"
## [1] "one variable, linear regression"
## [1] "bSigmaBest 7"
## [1] "naive effects model"
## [1] "one variable, linear regression naive effects model fit model:"
## 
## Call:
## lm(formula = formulaL, data = trainData)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.3721 -0.6891 -0.0037  0.6848  3.7826 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.20623    0.02260   9.125   <2e-16 ***
## x1           1.00000    0.03685  27.137   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.011 on 1998 degrees of freedom
## Multiple R-squared:  0.2693, Adjusted R-squared:  0.269 
## F-statistic: 736.4 on 1 and 1998 DF,  p-value: < 2.2e-16
## 
## [1] " train rmse 1.01025938596012"
## [1] " application rmse 0.999915402747535"
## [1] "one variable, linear regression naive effects model train rmse 1.01025938596012"

## TableGrob (3 x 2) "arrange": 5 grobs
##   z     cells    name                 grob
## 1 1 (2-2,1-1) arrange       gtable[layout]
## 2 2 (2-2,2-2) arrange       gtable[layout]
## 3 3 (3-3,1-1) arrange       gtable[layout]
## 4 4 (3-3,2-2) arrange       gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.1348]
## [1] "one variable, linear regression naive effects model test rmse 0.999915402747535"

## TableGrob (3 x 2) "arrange": 5 grobs
##   z     cells    name                 grob
## 1 1 (2-2,1-1) arrange       gtable[layout]
## 2 2 (2-2,2-2) arrange       gtable[layout]
## 3 3 (3-3,1-1) arrange       gtable[layout]
## 4 4 (3-3,2-2) arrange       gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.1501]
## [1] "effects model, sigma= 7"
## [1] "one variable, linear regression effects model, sigma= 7 fit model:"
## 
## Call:
## lm(formula = formulaL, data = trainData)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.3946 -0.6882 -0.0042  0.6863  3.7971 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.20571    0.02262   9.093   <2e-16 ***
## x1           1.00887    0.03731  27.039   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.012 on 1998 degrees of freedom
## Multiple R-squared:  0.2679, Adjusted R-squared:  0.2675 
## F-statistic: 731.1 on 1 and 1998 DF,  p-value: < 2.2e-16
## 
## [1] " train rmse 1.01124759697315"
## [1] " application rmse 1.00247594378318"
## [1] "one variable, linear regression Laplace noised 7 train rmse 1.01124759697315"

## TableGrob (3 x 2) "arrange": 5 grobs
##   z     cells    name                 grob
## 1 1 (2-2,1-1) arrange       gtable[layout]
## 2 2 (2-2,2-2) arrange       gtable[layout]
## 3 3 (3-3,1-1) arrange       gtable[layout]
## 4 4 (3-3,2-2) arrange       gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.1654]
## [1] "one variable, linear regression Laplace noised 7 test rmse 1.00247594378318"

## TableGrob (3 x 2) "arrange": 5 grobs
##   z     cells    name                 grob
## 1 1 (2-2,1-1) arrange       gtable[layout]
## 2 2 (2-2,2-2) arrange       gtable[layout]
## 3 3 (3-3,1-1) arrange       gtable[layout]
## 4 4 (3-3,2-2) arrange       gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.1807]
## [1] "effects model, jacknifed"
## [1] "one variable, linear regression effects model, jackknifed fit model:"
## 
## Call:
## lm(formula = formulaL, data = trainData)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.3933 -0.6946 -0.0039  0.6875  3.7985 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   0.2062     0.0227   9.084   <2e-16 ***
## x1            0.9871     0.0370  26.682   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.015 on 1998 degrees of freedom
## Multiple R-squared:  0.2627, Adjusted R-squared:  0.2623 
## F-statistic:   712 on 1 and 1998 DF,  p-value: < 2.2e-16
## 
## [1] " train rmse 1.01481235978284"
## [1] " application rmse 1.00008428967326"
## [1] "one variable, linear regression jackknifed train rmse 1.01481235978284"

## TableGrob (3 x 2) "arrange": 5 grobs
##   z     cells    name                 grob
## 1 1 (2-2,1-1) arrange       gtable[layout]
## 2 2 (2-2,2-2) arrange       gtable[layout]
## 3 3 (3-3,1-1) arrange       gtable[layout]
## 4 4 (3-3,2-2) arrange       gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.1960]
## [1] "one variable, linear regression jackknifed test rmse 1.00008428967326"

## TableGrob (3 x 2) "arrange": 5 grobs
##   z     cells    name                 grob
## 1 1 (2-2,1-1) arrange       gtable[layout]
## 2 2 (2-2,2-2) arrange       gtable[layout]
## 3 3 (3-3,1-1) arrange       gtable[layout]
## 4 4 (3-3,2-2) arrange       gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.2113]

## [1] "********"
## [1] "one variable, linear regression JackknifeModel"
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.9788  0.9962  1.0020  1.0020  1.0070  1.0230 
## [1] 0.007618995
## [1] "********"
## [1] "********"
## [1] "one variable, linear regression NaiveModel"
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.9786  0.9962  1.0020  1.0020  1.0070  1.0230 
## [1] 0.007634864
## [1] "********"
## [1] "********"
## [1] "one variable, linear regression NoisedModel"
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.9785  0.9964  1.0020  1.0020  1.0080  1.0230 
## [1] 0.007761853
## [1] "********"

## [1] "*************************************************************"

one variable plus noise variable, linear regression

## [1] "*************************************************************"
## [1] "one variable plus noise variable, linear regression"
## [1] "bSigmaBest 13"
## [1] "naive effects model"
## [1] "one variable plus noise variable, linear regression naive effects model fit model:"
## 
## Call:
## lm(formula = formulaL, data = trainData)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.9216 -0.6181  0.0055  0.6225  3.5298 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.20622    0.02058   10.02   <2e-16 ***
## x1           0.83459    0.03452   24.17   <2e-16 ***
## n1           0.78131    0.03844   20.33   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9203 on 1997 degrees of freedom
## Multiple R-squared:  0.3946, Adjusted R-squared:  0.394 
## F-statistic: 650.8 on 2 and 1997 DF,  p-value: < 2.2e-16
## 
## [1] " train rmse 0.919591353886876"
## [1] " application rmse 1.12246743812363"
## [1] "one variable plus noise variable, linear regression naive effects model train rmse 0.919591353886876"

## TableGrob (3 x 2) "arrange": 5 grobs
##   z     cells    name                 grob
## 1 1 (2-2,1-1) arrange       gtable[layout]
## 2 2 (2-2,2-2) arrange       gtable[layout]
## 3 3 (3-3,1-1) arrange       gtable[layout]
## 4 4 (3-3,2-2) arrange       gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.2556]
## [1] "one variable plus noise variable, linear regression naive effects model test rmse 1.12246743812363"

## TableGrob (3 x 2) "arrange": 5 grobs
##   z     cells    name                 grob
## 1 1 (2-2,1-1) arrange       gtable[layout]
## 2 2 (2-2,2-2) arrange       gtable[layout]
## 3 3 (3-3,1-1) arrange       gtable[layout]
## 4 4 (3-3,2-2) arrange       gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.2709]
## [1] "effects model, sigma= 13"
## [1] "one variable plus noise variable, linear regression effects model, sigma= 13 fit model:"
## 
## Call:
## lm(formula = formulaL, data = trainData)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.4184 -0.6812 -0.0047  0.6766  3.7079 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 0.214130   0.022666   9.447   <2e-16 ***
## x1          1.001805   0.037121  26.988   <2e-16 ***
## n1          0.004473   0.001946   2.298   0.0216 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.011 on 1997 degrees of freedom
## Multiple R-squared:  0.2691, Adjusted R-squared:  0.2684 
## F-statistic: 367.7 on 2 and 1997 DF,  p-value: < 2.2e-16
## 
## [1] " train rmse 1.01039449972278"
## [1] " application rmse 1.01196381722931"
## [1] "one variable plus noise variable, linear regression Laplace noised 13 train rmse 1.01039449972278"

## TableGrob (3 x 2) "arrange": 5 grobs
##   z     cells    name                 grob
## 1 1 (2-2,1-1) arrange       gtable[layout]
## 2 2 (2-2,2-2) arrange       gtable[layout]
## 3 3 (3-3,1-1) arrange       gtable[layout]
## 4 4 (3-3,2-2) arrange       gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.2862]
## [1] "one variable plus noise variable, linear regression Laplace noised 13 test rmse 1.01196381722931"

## TableGrob (3 x 2) "arrange": 5 grobs
##   z     cells    name                 grob
## 1 1 (2-2,1-1) arrange       gtable[layout]
## 2 2 (2-2,2-2) arrange       gtable[layout]
## 3 3 (3-3,1-1) arrange       gtable[layout]
## 4 4 (3-3,2-2) arrange       gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.3015]
## [1] "effects model, jacknifed"
## [1] "one variable plus noise variable, linear regression effects model, jackknifed fit model:"
## 
## Call:
## lm(formula = formulaL, data = trainData)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.3986 -0.6920 -0.0077  0.6877  3.8126 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.20643    0.02268   9.101   <2e-16 ***
## x1           0.98425    0.03698  26.614   <2e-16 ***
## n1          -0.07739    0.03479  -2.224   0.0262 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.014 on 1997 degrees of freedom
## Multiple R-squared:  0.2645, Adjusted R-squared:  0.2638 
## F-statistic: 359.2 on 2 and 1997 DF,  p-value: < 2.2e-16
## 
## [1] " train rmse 1.01355772650768"
## [1] " application rmse 1.00913108707443"
## [1] "one variable plus noise variable, linear regression jackknifed train rmse 1.01355772650768"

## TableGrob (3 x 2) "arrange": 5 grobs
##   z     cells    name                 grob
## 1 1 (2-2,1-1) arrange       gtable[layout]
## 2 2 (2-2,2-2) arrange       gtable[layout]
## 3 3 (3-3,1-1) arrange       gtable[layout]
## 4 4 (3-3,2-2) arrange       gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.3168]
## [1] "one variable plus noise variable, linear regression jackknifed test rmse 1.00913108707443"

## TableGrob (3 x 2) "arrange": 5 grobs
##   z     cells    name                 grob
## 1 1 (2-2,1-1) arrange       gtable[layout]
## 2 2 (2-2,2-2) arrange       gtable[layout]
## 3 3 (3-3,1-1) arrange       gtable[layout]
## 4 4 (3-3,2-2) arrange       gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.3321]

## [1] "********"
## [1] "one variable plus noise variable, linear regression JackknifeModel"
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.9789  0.9991  1.0040  1.0030  1.0080  1.0220 
## [1] 0.007373575
## [1] "********"
## [1] "********"
## [1] "one variable plus noise variable, linear regression NaiveModel"
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.100   1.121   1.133   1.133   1.142   1.184 
## [1] 0.01482979
## [1] "********"
## [1] "********"
## [1] "one variable plus noise variable, linear regression NoisedModel"
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.9809  1.0030  1.0070  1.0080  1.0130  1.0430 
## [1] 0.008257433
## [1] "********"

## [1] "*************************************************************"

one variable plus noise variable, diagonal regression

## [1] "*************************************************************"
## [1] "one variable plus noise variable, diagonal regression"
## [1] "bSigmaBest 21"
## [1] "naive effects model"
## [1] "one variable plus noise variable, diagonal regression naive effects model fit model:"
##       x1       n1 
## 1.000005 1.000333 
## [1] " train rmse 0.958540237968956"
## [1] " application rmse 1.20618715828122"
## [1] "one variable plus noise variable, diagonal regression naive effects model train rmse 0.958540237968956"

## TableGrob (3 x 2) "arrange": 5 grobs
##   z     cells    name                 grob
## 1 1 (2-2,1-1) arrange       gtable[layout]
## 2 2 (2-2,2-2) arrange       gtable[layout]
## 3 3 (3-3,1-1) arrange       gtable[layout]
## 4 4 (3-3,2-2) arrange       gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.3764]
## [1] "one variable plus noise variable, diagonal regression naive effects model test rmse 1.20618715828122"

## TableGrob (3 x 2) "arrange": 5 grobs
##   z     cells    name                 grob
## 1 1 (2-2,1-1) arrange       gtable[layout]
## 2 2 (2-2,2-2) arrange       gtable[layout]
## 3 3 (3-3,1-1) arrange       gtable[layout]
## 4 4 (3-3,2-2) arrange       gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.3917]
## [1] "effects model, sigma= 21"
## [1] "one variable plus noise variable, diagonal regression effects model, sigma= 21 fit model:"
##          x1          n1 
## 0.985208771 0.002843946 
## [1] " train rmse 1.03724740931406"
## [1] " application rmse 1.03988323905615"
## [1] "one variable plus noise variable, diagonal regression Laplace noised 21 train rmse 1.03724740931406"

## TableGrob (3 x 2) "arrange": 5 grobs
##   z     cells    name                 grob
## 1 1 (2-2,1-1) arrange       gtable[layout]
## 2 2 (2-2,2-2) arrange       gtable[layout]
## 3 3 (3-3,1-1) arrange       gtable[layout]
## 4 4 (3-3,2-2) arrange       gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.4070]
## [1] "one variable plus noise variable, diagonal regression Laplace noised 21 test rmse 1.03988323905615"

## TableGrob (3 x 2) "arrange": 5 grobs
##   z     cells    name                 grob
## 1 1 (2-2,1-1) arrange       gtable[layout]
## 2 2 (2-2,2-2) arrange       gtable[layout]
## 3 3 (3-3,1-1) arrange       gtable[layout]
## 4 4 (3-3,2-2) arrange       gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.4223]
## [1] "effects model, jacknifed"
## [1] "one variable plus noise variable, diagonal regression effects model, jackknifed fit model:"
##         x1         n1 
##  0.9871528 -0.1088369 
## [1] " train rmse 1.03458802692346"
## [1] " application rmse 1.03176880530955"
## [1] "one variable plus noise variable, diagonal regression jackknifed train rmse 1.03458802692346"

## TableGrob (3 x 2) "arrange": 5 grobs
##   z     cells    name                 grob
## 1 1 (2-2,1-1) arrange       gtable[layout]
## 2 2 (2-2,2-2) arrange       gtable[layout]
## 3 3 (3-3,1-1) arrange       gtable[layout]
## 4 4 (3-3,2-2) arrange       gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.4376]
## [1] "one variable plus noise variable, diagonal regression jackknifed test rmse 1.03176880530955"

## TableGrob (3 x 2) "arrange": 5 grobs
##   z     cells    name                 grob
## 1 1 (2-2,1-1) arrange       gtable[layout]
## 2 2 (2-2,2-2) arrange       gtable[layout]
## 3 3 (3-3,1-1) arrange       gtable[layout]
## 4 4 (3-3,2-2) arrange       gtable[layout]
## 5 5 (1-1,1-2) arrange text[GRID.text.4529]

## [1] "********"
## [1] "one variable plus noise variable, diagonal regression JackknifeModel"
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.9988  1.0180  1.0220  1.0220  1.0270  1.0410 
## [1] 0.007441217
## [1] "********"
## [1] "********"
## [1] "one variable plus noise variable, diagonal regression NaiveModel"
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.167   1.207   1.219   1.220   1.236   1.275 
## [1] 0.01935423
## [1] "********"
## [1] "********"
## [1] "one variable plus noise variable, diagonal regression NoisedModel"
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.9996  1.0220  1.0290  1.0300  1.0360  1.1360 
## [1] 0.01344902
## [1] "********"

## [1] "*************************************************************"